Associations Between Mobility Indices and the COVID-19 Pandemic in Thailand

Main Article Content

Pitchaporn Inthisorn
Nattapong Puttanapong


This study aims to examine the associations between the Coronavirus disease 2019 (COVID-19) pandemic and alternative indicators. Specifically, Apple mobility index, Google community mobility index, and Nighttime-light (NTL) data are used for empirical analyses using ordinary least squares (OLS) and panel regressions as research methods. Results produced by OLS models show that Apple’s subcategory of driving activity and Google’s subcategory of visiting transit places are negatively associated with the number of COVID-19 cases. To extend the spatiotemporal details of this analysis, we formulate the panel data by integrating the monthly provincial indicators of Apple mobility index, NTL index, and the COVID-19 infected cases. Both fixed- and random-effects panel regression models indicate that Apple’s driving and walking mobility subcategories are negatively associated with the COVID-19 infected cases. By contrast, the relationship between the NTL index and the intensity of the COVID-19 outbreak is inconclusive. These findings suggest that Apple's mobility index can be applied as an alternative and timely indicator of economic activity, particularly for observing the near real-time intensity of mobility and transportation volume. In addition, these findings can serve as a resource for developing spatial models for urban planning and geographical impacts.


Download data is not yet available.

Article Details

How to Cite
Inthisorn, P., & Puttanapong, N. (2022). Associations Between Mobility Indices and the COVID-19 Pandemic in Thailand. Nakhara : Journal of Environmental Design and Planning, 21(2), Article 215.
Research Articles


Almagro, M., & Orane-Hutchinson, A. (2022). JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time. Journal of Urban Economics, 127, 103293.

Badr, H. S., Du, H., Marshall, M., Dong, E., Squire, M. M., & Gardner, L. M. (2020). Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. The Lancet Infectious Diseases, 20(11), 1247-1254.

Camba, A. C. J., & Camba, A. L. (2020). The effects of restrictions in economic activity on the spread of COVID-19 in the Philippines: Insights from Apple and Google mobility indicators. The Journal of Asian Finance, Economics and Business, 7(12), 115-121.

Chan, J. Y., Leow, S. M., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z.W., & Chen, Y.L. (2022). Mitigating the multicollinearity problem and its machine learning approach: A review. Mathematics, 10(8).

Chen, G. J. (2012). A simple way to deal with multicollinearity. Journal of Applied Statistics, 39(9), 1893-1909.

Chiou, L., & Tucker, C. (2020). Social distancing, internet access and inequality. National Bureau of Economic Research Working Paper Series, No. 26982.

Fang, H., Wang, L., & Yang, Y. (2020). Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China. Journal of Public Economics, 191, 104272.

Fauver, J. R., Petrone, M. E., Hodcroft, E. B., Shioda, K., Ehrlich, H. Y., Watts, A. G., Vogels, C. B. F., Brito, A. F., Alpert, T., Muyombwe, A., Razeq, J., Downing, R., Cheemarla, N. R., Wyllie, A. L., Kalinich, C. C., Ott, I. M., Quick, J., Loman, N. J., Neugebauer, K. M., . . . Grubaugh, N. D. (2020). Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Cell, 181(5), 990-996.e995.

Haase, D., Lautenbach, S., & Seppelt, R. (2010). Modeling and simulating residential mobility in a shrinking city using an agent-based approach. Environmental Modelling & Software, 25(10), 1225-1240.

Hasan, S., & Ukkusuri, S. V. (2014). Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44, 363-381.

Hermes, K., & Poulsen, M. (2012). A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Computers, Environment and Urban Systems, 36(4), 281-290.

Holmdahl, I., & Buckee, C. (2020). Wrong but useful — What Covid-19 epidemiologic models can and cannot tell us. New England Journal of Medicine, 383(4), 303-305.

Huang, X., Li, Z., Jiang, Y., Li, X., & Porter, D. (2020). Twitter reveals human mobility dynamics during the COVID-19 pandemic. PLoS One, 15(11), e0241957.

Huang, X., Wang, C., & Li, Z. (2018). Reconstructing flood inundation probability by enhancing near real-time imagery with real-time gauges and tweets. IEEE Transactions on Geoscience and Remote Sensing, 56, 4691-4701.

Isaacman, S., Becker, R., Cáceres, R., Kobourov, S., Martonosi, M., Rowland, J., & Varshavsky, A. (2011). Identifying important places in people’s lives from cellular network data. In K. Lyons, J. Hightower, & E.M. Huang (Eds.), Pervasive 2011: Pervasive Computing. Springer.

Jiang, Y., Li, Z., & Ye, X. (2019). Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level. Cartography and Geographic Information Science, 46(3), 228-242.

Kartal, M. T. (2021). The effect of COVID-19 pandemic on oil prices: Daily evidence from Turkey. Energy Research Letters,1(4), 1-4.

Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D. M., null, n., du Plessis, L., Faria, N. R., Li, R., Hanage, W. P., Brownstein, J. S., Layan, M., Vespignani, A., Tian, H., Dye, C., Pybus, O. G., & Scarpino, S. V. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497.

Li, Z., Huang, X., ye, X., & Li, X. (2020). ODT flow explorer: Extract, query, and visualize human mobility. ArXiv:2011.12958. ARXVI.

Lou, J., Shen, X., & Niemeier, D. (2020). Are stay-at-home orders more difficult to follow for low-income groups? Journal of Transport Geography, 89, 102894.

Luenam, A., & Puttanapong, N. (2020). Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015. Geospat Health, 15(2), 217-224.

Ma, J., Heppenstall, A., Harland, K., & Mitchell, G. (2014). Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO2 from urban travel in Beijing. Computers, Environment and Urban Systems, 45, 78-88.

Martín, Y., Cutter, S., & Li, Z. (2019). Bridging Twitter and survey data for the evacuation assessment of Hurricane Matthew and Hurricane Irma. Natural Hazards Review.

Oh, J., Lee, H.-Y., Khuong, Q. L., Markuns, J. F., Bullen, C., Barrios, O. E. A., Hwang, S.-s., Suh, Y. S., McCool, J., Kachur, S. P., Chan, C.-C., Kwon, S., Kondo, N., Hoang, V. M., Moon, J. R., Rostila, M., Norheim, O. F., You, M., Withers, M., . . . Gostin, L. O. (2021). Mobility restrictions were associated with reductions in COVID-19 incidence early in the pandemic: Evidence from a real-time evaluation in 34 countries. Scientific Reports, 11(1), 13717.

Oliver, N., Letouzé, E., Sterly, H., Delataille, S., De Nadai, M., Lepri, B., Lambiotte, R., Benjamins, R., Cattuto, C., Colizza, V., Cordes, N. d., Fraiberger, S. P., Koebe, T., Lehmann, S., Murillo, J., Pentland, A. S., Pham, P. N., Pivetta, F., Salah, A. A., . . . Vinck, P. (2020). Mobile phone data and COVID-19: Missing an opportunity? ArXiv: 2003.12347. ARXIV.

Osewe, P. (2021). Pandemic preparedness and response strategies: COVID-19 lessons from the Republic of Korea, Thailand, and Viet Nam. Asian Development Bank.

Pei, S., Kandula, S., & Shaman, J. (2020). Differential effects of intervention timing on COVID-19 spread in the United States. MedRxiv.

Puttanapong, N., Martinez, A., Bulan, J. A., Addawe, M., Durante, R. L., & Martillan, M. (2022). Predicting poverty using geospatial data in Thailand. ISPRS International Journal of Geo-Information, 11(5).

Sangkasem, K., & Puttanapong, N. (2022). Analysis of spatial inequality using DMSP-OLS nighttime-light satellite imageries: A case study of Thailand. Regional Science Policy & Practice, 14(4), 828-849.

Sen, S., Karaca-Mandic, P., & Georgiou, A. (2020). Association of stay-at-home orders with COVID-19 hospitalizations in 4 states. JAMA, 323(24), 2522-2524.

Sills, J., Buckee, C. O., Balsari, S., Chan, J., Crosas, M., Dominici, F., Gasser, U., Grad, Y. H., Grenfell, B., Halloran, M. E., Kraemer, M. U. G., Lipsitch, M., Metcalf, C. J. E., Meyers, L. A., Perkins, T. A., Santillana, M., Scarpino, S. V., Viboud, C., Wesolowski, A., & Schroeder, A. (2020). Aggregated mobility data could help fight COVID-19. Science, 368(6487), 145-146.

Sirkeci, I., & Yucesahin, M. M. (2020). Coronavirus and migration: Analysis of human mobility and the spread of Covid-19. Migration Letters, 17(2), 379-398.

Subbiah, R., Lum, K., Marathe, A., & Marathe, M. (2013). Activity based energy demand modeling for residential buildings. IEEE PES Innovative Smart Grid Technologies Conference (ISGT), (pp. 1-6).

Sulyok, M., & Walker, M. D. (2021). Mobility and COVID-19 mortality across Scandinavia: A modeling study. Travel Medicineand Infectious Disease, 41, 102039.

Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., Wang, B., Yang, P., Cui, Y., Song, Y., Zheng, P., Wang, Q., Bjornstad, O. N., Yang, R., Grenfell, B. T., . . . Dye, C. (2020). An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science, 368(6491), 638-642.

Wang, Y., Yuan, N., Lian, D., Xu, L., Xie, X., Chen, E., & Rui, Y. (2015). Regularity and conformity. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Sydney, NSW, Australia. Association for Computing Machinery (pp. 1275-1284).

World Health Organization. (2020). WHO Thailand situation report - 1. WHO.

World Health Organization. (2021). WHO Thailand situation report - 201.WHO.

World Health Organization. (2022). Coronavirus disease (COVID-19) weekly epidemiological update and weekly operational update. WHO.

Wu, L., Zhi, Y., Sui, Z., & Liu, Y. (2014). Intra-urban human mobility and activity transition: Evidence from social media check-in data. PLoS One, 9(5), e97010.

Xu, S., & Li, Y. (2020). Beware of the second wave of COVID-19. The Lancet, 395(10233), 1321-1322.

Yang, C., Sha, D., Liu, Q., Li, Y., Lan, H., Guan, W. W., Hu, T., Li, Z., Zhang, Z., Thompson, J. H., Wang, Z., Wong, D., Ruan, S., Yu, M., Richardson, D., Zhang, L., Hou, R., Zhou, Y., Zhong, C., . . . Ding, A. (2020). Taking the pulse of COVID-19: A spatiotemporal perspective. International Journal of Digital Earth, 13(10), 1186-1211.

Zhang, D., Huang, J., Li, Y., Zhang, F., Xu, C.-Z., & He, T. (2014). Exploring human mobility with multi-source data at extremely large metropolitan scales. MobiCom'14: The 20th Annual International Conference on Mobile Computing and Networking (pp. 201-212). Association for Computing Machinery.